class_docstr: A posterior based on GPyTorch's multi-variate Normal distributions.
docstr: Compute the quantiles of the marginal distributions.
docstr_tok: ["Compute","the","quantiles","of","the","marginal","distributions","."]
code: def quantile(self, value: Tensor) -> Tensor:
      r"""Compute the quantiles of the marginal distributions."""
      if value.numel() > 1:
          return torch.stack([self.quantile(v) for v in value], dim=0)
      marginal = Normal(loc=self.mean, scale=self.variance.sqrt())
      return marginal.icdf(value)
  
imports: from __future__ import annotations
  import warnings
  from contextlib import ExitStack
  from typing import Optional, Tuple, TYPE_CHECKING, Union
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.posteriors.base_samples import _reshape_base_samples_non_interleaved
  from botorch.posteriors.torch import TorchPosterior
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator import settings
  from linear_operator.operators import BlockDiagLinearOperator, DenseLinearOperator, LinearOperator, SumLinearOperator
  from torch import Tensor
  from torch.distributions import Normal
label_desc: Use image node_id 10 for calling the GPyTorchPosterior obj's underlying member method code with example usage: obj.quantile(value) and returns: marginal, torch